ITK  5.2.0
Insight Toolkit
Examples/Segmentation/GeodesicActiveContourShapePriorLevelSetImageFilter.cxx
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
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*=========================================================================*/
// Software Guide : BeginLatex
//
// In medical imaging applications, the general shape, location and
// orientation of an anatomical structure of interest is typically
// known \emph{a priori}. This information can be used to aid the
// segmentation process especially when image contrast is low or
// when the object boundary is not distinct.
//
// In \cite{Leventon2000}, Leventon \emph{et al.} extended the
// geodesic active contours method with an additional shape-influenced term in
// the driving PDE. The
// \doxygen{GeodesicActiveContourShapePriorLevelSetFilter} is a generalization
// of Leventon's approach and its use is illustrated in the following example.
//
// To support shape-guidance, the generic level set
// equation (Eqn(~\ref{eqn:LevelSetEquation})) is extended to incorporate a
// shape guidance term:
//
// \begin{equation}
// \label{eqn:ShapeInfluenceTerm}
// \xi \left(\psi^{*}(\mathbf{x}) - \psi(\mathbf{x})\right)
// \end{equation}
//
// where $\psi^{*}$ is the signed distance function of the ``best-fit'' shape
// with respect to a shape model. The new term has the effect of driving the
// contour towards the best-fit shape. The scalar $\xi$ weights the influence
// of the shape term in the overall evolution. In general, the best-fit shape
// is not known ahead of time and has to be iteratively estimated in
// conjunction with the contour evolution.
//
// As with the \doxygen{GeodesicActiveContourLevelSetImageFilter}, the
// GeodesicActiveContourShapePriorLevelSetImageFilter expects two input
// images: the first is an initial level set and the second a feature image
// that represents the image edge potential. The configuration of this
// example is quite similar to the example in
// Section~\ref{sec:GeodesicActiveContourImageFilter} and hence the
// description will focus on the new objects involved in the segmentation
// process as shown in
// Figure~\ref{fig:GeodesicActiveContourShapePriorCollaborationDiagram}.
//
// \begin{figure} \center
// \includegraphics[width=\textwidth]{GeodesicActiveContourShapePriorCollaborationDiagram}
// \itkcaption[GeodesicActiveContourShapePriorLevelSetImageFilter
// collaboration diagram]{Collaboration diagram for the
// GeodesicActiveContourShapePriorLevelSetImageFilter applied to a
// segmentation task.}
// \label{fig:GeodesicActiveContourShapePriorCollaborationDiagram}
// \end{figure}
//
// The process pipeline begins with centering the input image using the
// the \doxygen{ChangeInformationImageFilter} to simplify the estimation of
// the pose of the shape, to be explained later. The centered image is then
// smoothed using non-linear diffusion to remove noise and the gradient
// magnitude is computed from the smoothed image. For simplicity, this example
// uses the \doxygen{BoundedReciprocalImageFilter} to produce the edge
// potential image.
//
// The \doxygen{FastMarchingImageFilter} creates an initial level set using
// three user specified seed positions and a initial contour radius. Three
// seeds are used in this example to facilitate the segmentation of long
// narrow objects in a smaller number of iterations. The output of the
// FastMarchingImageFilter is passed as the input to the
// GeodesicActiveContourShapePriorLevelSetImageFilter. At then end of the
// segmentation process, the output level set is passed to the
// \doxygen{BinaryThresholdImageFilter} to produce a binary mask representing
// the segmented object.
//
// The remaining objects in
// Figure~\ref{fig:GeodesicActiveContourShapePriorCollaborationDiagram}
// are used for shape modeling and estimation.
// The \doxygen{PCAShapeSignedDistanceFunction} represents a statistical
// shape model defined by a mean signed distance and the first $K$
// principal components modes; while the \doxygen{Euler2DTransform} is used
// to represent the pose of the shape. In this implementation, the
// best-fit shape estimation problem is reformulated as a minimization problem
// where the \doxygen{ShapePriorMAPCostFunction} is the cost function to
// be optimized using the \doxygen{OnePlusOneEvolutionaryOptimizer}.
//
// It should be noted that, although particular shape model, transform
// cost function, and optimizer are used in this example, the implementation
// is generic, allowing different instances of these components to be
// plugged in. This flexibility allows a user to tailor the behavior of the
// segmentation process to suit the circumstances of the targeted application.
//
// Let's start the example by including the headers of the new filters
// involved in the segmentation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, we include the headers of the objects involved in shape
// modeling and estimation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Given the numerous parameters involved in tuning this segmentation method
// it is not uncommon for a segmentation process to
// run for several minutes and still produce an unsatisfactory result. For
// debugging purposes it is quite helpful to track the evolution of the
// segmentation as it progresses. The following defines a
// custom \doxygen{Command} class
// for monitoring the RMS change and shape parameters at each iteration.
//
// \index{itk::Geodesic\-Active\-Contour\-Shape\-Prior\-LevelSet\-Image\-Filter!Monitoring}
// \index{itk::Shape\-Prior\-Segmentation\-Level\-Set\-Image\-Filter!Monitoring}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkCommand.h"
template <class TFilter>
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
const auto * filter = static_cast<const TFilter *>(object);
if (typeid(event) != typeid(itk::IterationEvent))
{
return;
}
std::cout << filter->GetElapsedIterations() << ": ";
std::cout << filter->GetRMSChange() << " ";
std::cout << filter->GetCurrentParameters() << std::endl;
}
};
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 18)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage";
std::cerr << " seed1X seed1Y";
std::cerr << " seed2X seed2Y";
std::cerr << " seed3X seed3Y";
std::cerr << " initialDistance";
std::cerr << " sigma";
std::cerr << " propagationScaling shapePriorScaling";
std::cerr << " meanShapeImage numberOfModes shapeModeFilePattern";
std::cerr << " startX startY" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We define the image type using a particular pixel type and
// dimension. In this case we will use 2D \code{float} images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InternalPixelType = float;
constexpr unsigned int Dimension = 2;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : EndCodeSnippet
// The following lines instantiate the thresholding filter that will
// process the final level set at the output of the
// GeodesicActiveContourLevelSetImageFilter.
//
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using ThresholdingFilterType =
ThresholdingFilterType::Pointer thresholder = ThresholdingFilterType::New();
thresholder->SetLowerThreshold(-1000.0);
thresholder->SetUpperThreshold(0.0);
thresholder->SetOutsideValue(0);
thresholder->SetInsideValue(255);
// We instantiate reader and writer types in the following lines.
//
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName(argv[1]);
writer->SetFileName(argv[2]);
// The RescaleIntensityImageFilter type is declared below. This filter will
// renormalize image before sending them to writers.
//
using CastFilterType =
// The \doxygen{CurvatureAnisotropicDiffusionImageFilter} type is
// instantiated using the internal image type.
//
using SmoothingFilterType =
InternalImageType>;
SmoothingFilterType::Pointer smoothing = SmoothingFilterType::New();
// The types of the
// GradientMagnitudeRecursiveGaussianImageFilter is
// instantiated using the internal image type.
//
using GradientFilterType =
InternalImageType>;
GradientFilterType::Pointer gradientMagnitude = GradientFilterType::New();
// We declare now the type of the FastMarchingImageFilter that
// will be used to generate the initial level set in the form of a distance
// map.
//
using FastMarchingFilterType =
// Next we construct one filter of this class using the \code{New()}
// method.
//
FastMarchingFilterType::Pointer fastMarching =
FastMarchingFilterType::New();
// Software Guide : BeginLatex
//
// The following line instantiate a
// \doxygen{GeodesicActiveContourShapePriorLevelSetImageFilter}
// using the \code{New()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using GeodesicActiveContourFilterType =
InternalImageType,
InternalImageType>;
GeodesicActiveContourFilterType::Pointer geodesicActiveContour =
GeodesicActiveContourFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{ChangeInformationImageFilter} is the first filter in the
// preprocessing stage and is used to force the image origin to the center
// of the image.
//
// \index{itk::ChangeInformationImageFilter!CenterImageOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using CenterFilterType =
CenterFilterType::Pointer center = CenterFilterType::New();
center->CenterImageOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example, we will use the bounded reciprocal $1/(1+x)$ of
// the image gradient magnitude as the edge potential feature image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ReciprocalFilterType =
ReciprocalFilterType::Pointer reciprocal = ReciprocalFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the GeodesicActiveContourShapePriorLevelSetImageFilter, scaling
// parameters are used to trade off between the propagation (inflation),
// the curvature (smoothing), the advection, and the shape influence terms.
// These parameters are set
// using methods \code{SetPropagationScaling()},
// \code{SetCurvatureScaling()}, \code{SetAdvectionScaling()} and
// \code{SetShapePriorScaling()}. In this
// example, we will set the curvature and advection scales to one and let
// the propagation and shape prior scale be command-line arguments.
//
// \index{itk::Geodesic\-Active\-Contour\-Shape\-Prior\-LevelSet\-Image\-Filter!SetPropagationScaling()}
// \index{itk::Shape\-Prior\-Segmentation\-Level\-Set\-Image\-Filter!SetPropagationScaling()}
// \index{itk::Geodesic\-Active\-Contour\-Shape\-Prior\-LevelSet\-Image\-Filter!SetCurvatureScaling()}
// \index{itk::Shape\-Prior\-Segmentation\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
// \index{itk::Geodesic\-Active\-Contour\-Shape\-Prior\-LevelSet\-Image\-Filter!SetAdvectionScaling()}
// \index{itk::Shape\-Prior\-Segmentation\-Level\-Set\-Image\-Filter!SetAdvectionScaling()}
//
// Software Guide : EndLatex
const double propagationScaling = std::stod(argv[11]);
const double shapePriorScaling = std::stod(argv[12]);
// Software Guide : BeginCodeSnippet
geodesicActiveContour->SetPropagationScaling(propagationScaling);
geodesicActiveContour->SetShapePriorScaling(shapePriorScaling);
geodesicActiveContour->SetCurvatureScaling(1.0);
geodesicActiveContour->SetAdvectionScaling(1.0);
// Software Guide : EndCodeSnippet
// Once activiated the level set evolution will stop if the convergence
// criteria or if the maximum number of iterations is reached. The
// convergence criteria is defined in terms of the root mean squared (RMS)
// change in the level set function. The evolution is said to have
// converged if the RMS change is below a user specified threshold. In a
// real application is desirable to couple the evolution of the zero set
// to a visualization module allowing the user to follow the evolution of
// the zero set. With this feedback, the user may decide when to stop the
// algorithm before the zero set leaks through the regions of low gradient
// in the contour of the anatomical structure to be segmented.
geodesicActiveContour->SetMaximumRMSError(0.005);
geodesicActiveContour->SetNumberOfIterations(400);
// Software Guide : BeginLatex
//
// Each iteration, the current ``best-fit'' shape is estimated from the
// edge potential image and the current contour. To increase speed, only
// information within the sparse field layers of the current contour is
// used in the estimation. The default number of sparse field layers is the
// same as the ImageDimension which does not contain enough information to
// get a reliable best-fit shape estimate. Thus, we override the default
// and set the number of layers to 4.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
geodesicActiveContour->SetNumberOfLayers(4);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filters are then connected in a pipeline as illustrated in
// Figure~\ref{fig:GeodesicActiveContourShapePriorCollaborationDiagram}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
center->SetInput(reader->GetOutput());
smoothing->SetInput(center->GetOutput());
gradientMagnitude->SetInput(smoothing->GetOutput());
reciprocal->SetInput(gradientMagnitude->GetOutput());
geodesicActiveContour->SetInput(fastMarching->GetOutput());
geodesicActiveContour->SetFeatureImage(reciprocal->GetOutput());
thresholder->SetInput(geodesicActiveContour->GetOutput());
writer->SetInput(thresholder->GetOutput());
// Software Guide : EndCodeSnippet
// The CurvatureAnisotropicDiffusionImageFilter requires a couple of
// parameter to be defined. The following are typical values for $2D$
// images. However they may have to be adjusted depending on the amount of
// noise present in the input image. This filter has been discussed in
// section~\ref{sec:GradientAnisotropicDiffusionImageFilter}.
smoothing->SetTimeStep(0.125);
smoothing->SetNumberOfIterations(5);
smoothing->SetConductanceParameter(9.0);
// The GradientMagnitudeRecursiveGaussianImageFilter performs the
// equivalent of a convolution with a Gaussian kernel, followed by a
// derivative operator. The sigma of this Gaussian can be used to control
// the range of influence of the image edges. This filter has been
// discussed in
// Section~\ref{sec:GradientMagnitudeRecursiveGaussianImageFilter}.
const double sigma = std::stod(argv[10]);
gradientMagnitude->SetSigma(sigma);
// The FastMarchingImageFilter requires the user to provide a seed
// point from which the level set will be generated. The user can actually
// pass not only one seed point but a set of them. Note that the
// FastMarchingImageFilter is used here only as a helper in the
// determination of an initial level set. We could have used the
// \doxygen{DanielssonDistanceMapImageFilter} in the same way.
//
// The seeds are passed stored in a container. The type of this
// container is defined as \code{NodeContainer} among the
// FastMarchingImageFilter traits.
//
using NodeContainer = FastMarchingFilterType::NodeContainer;
using NodeType = FastMarchingFilterType::NodeType;
NodeContainer::Pointer seeds = NodeContainer::New();
seedPosition[0] = std::stoi(argv[3]);
seedPosition[1] = std::stoi(argv[4]);
// Nodes are created as stack variables and initialized with a value and an
// \doxygen{Index} position. Note that here we assign the value of minus
// the user-provided distance to the unique node of the seeds passed to the
// FastMarchingImageFilter. In this way, the value will increment
// as the front is propagated, until it reaches the zero value
// corresponding to the contour. After this, the front will continue
// propagating until it fills up the entire image. The initial distance is
// taken here from the command line arguments. The rule of thumb for the
// user is to select this value as the distance from the seed points at
// which she want the initial contour to be.
const double initialDistance = std::stod(argv[9]);
NodeType node;
const double seedValue = -initialDistance;
node.SetValue(seedValue);
node.SetIndex(seedPosition);
// The list of nodes is initialized and then every node is inserted using
// the \code{InsertElement()}.
seeds->Initialize();
seeds->InsertElement(0, node);
seedPosition[0] = std::stoi(argv[5]);
seedPosition[1] = std::stoi(argv[6]);
node.SetIndex(seedPosition);
seeds->InsertElement(1, node);
seedPosition[0] = std::stoi(argv[7]);
seedPosition[1] = std::stoi(argv[8]);
node.SetIndex(seedPosition);
seeds->InsertElement(2, node);
// The set of seed nodes is passed now to the
// FastMarchingImageFilter with the method
// \code{SetTrialPoints()}.
//
fastMarching->SetTrialPoints(seeds);
// Since the FastMarchingImageFilter is used here just as a
// Distance Map generator. It does not require a speed image as input.
// Instead the constant value $1.0$ is passed using the
// \code{SetSpeedConstant()} method.
//
fastMarching->SetSpeedConstant(1.0);
// Here we configure all the writers required to see the intermediate
// outputs of the pipeline. This is added here only for
// pedagogical/debugging purposes. These intermediate output are normaly
// not required. Only the output of the final thresholding filter should be
// relevant. Observing intermediate output is helpful in the process of
// fine tuning the parameters of filters in the pipeline.
//
CastFilterType::Pointer caster1 = CastFilterType::New();
CastFilterType::Pointer caster2 = CastFilterType::New();
CastFilterType::Pointer caster3 = CastFilterType::New();
CastFilterType::Pointer caster4 = CastFilterType::New();
WriterType::Pointer writer1 = WriterType::New();
WriterType::Pointer writer2 = WriterType::New();
WriterType::Pointer writer3 = WriterType::New();
WriterType::Pointer writer4 = WriterType::New();
caster1->SetInput(smoothing->GetOutput());
writer1->SetInput(caster1->GetOutput());
writer1->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput1.png");
caster1->SetOutputMinimum(0);
caster1->SetOutputMaximum(255);
writer1->Update();
caster2->SetInput(gradientMagnitude->GetOutput());
writer2->SetInput(caster2->GetOutput());
writer2->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput2.png");
caster2->SetOutputMinimum(0);
caster2->SetOutputMaximum(255);
writer2->Update();
caster3->SetInput(reciprocal->GetOutput());
writer3->SetInput(caster3->GetOutput());
writer3->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput3.png");
caster3->SetOutputMinimum(0);
caster3->SetOutputMaximum(255);
writer3->Update();
caster4->SetInput(fastMarching->GetOutput());
writer4->SetInput(caster4->GetOutput());
writer4->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput4.png");
caster4->SetOutputMinimum(0);
caster4->SetOutputMaximum(255);
// The FastMarchingImageFilter requires the user to specify the
// size of the image to be produced as output. This is done using the
// \code{SetOutputRegion()}. Note that the size is obtained here from the
// output image of the centering filter. The size of this image is valid
// only after the \code{Update()} methods of this filter has been called
// directly or indirectly.
//
fastMarching->SetOutputRegion(center->GetOutput()->GetBufferedRegion());
fastMarching->SetOutputSpacing(center->GetOutput()->GetSpacing());
fastMarching->SetOutputOrigin(center->GetOutput()->GetOrigin());
// Software Guide : BeginLatex
//
// Next, we define the shape model. In this example,
// we use an implicit shape model based on the principal components
// such that:
//
// \begin{equation}
// \psi^{*}(\mathbf{x}) = \mu(\mathbf{x}) + \sum_k \alpha_k u_k(\mathbf{x})
// \end{equation}
//
// where $\mu(\mathbf{x})$ is the mean signed distance computed from
// training set of segmented objects and $u_k(\mathbf{x})$ are the first
// $K$ principal components of the offset (signed distance - mean). The
// coefficients $\{\alpha_k\}$ form the set of \emph{shape} parameters.
//
// Given a set of training data, the \doxygen{ImagePCAShapeModelEstimator}
// can be used to obtain
// the mean and principal mode shape images required by
// PCAShapeSignedDistanceFunction.
//
// \index{itk::PCAShapeSignedDistanceFunction!New()}
// \index{itk::PCAShapeSignedDistanceFunction!SetNumberOfPrincipalComponents()}
//
//
// Software Guide : EndLatex
const unsigned int numberOfPCAModes = std::stoi(argv[14]);
// Software Guide : BeginCodeSnippet
using ShapeFunctionType =
ShapeFunctionType::Pointer shape = ShapeFunctionType::New();
shape->SetNumberOfPrincipalComponents(numberOfPCAModes);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example, we will read the mean shape and
// principal mode images from file. We will assume that
// the filenames of the mode images form a numeric series starting from
// index 0.
//
// \index{itk::PCAShapeSignedDistanceFunction!SetMeanImage()}
// \index{itk::PCAShapeSignedDistanceFunction!SetPrincipalComponentsImages()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ReaderType::Pointer meanShapeReader = ReaderType::New();
meanShapeReader->SetFileName(argv[13]);
meanShapeReader->Update();
std::vector<InternalImageType::Pointer> shapeModeImages(numberOfPCAModes);
fileNamesCreator->SetStartIndex(0);
fileNamesCreator->SetEndIndex(numberOfPCAModes - 1);
fileNamesCreator->SetSeriesFormat(argv[15]);
const std::vector<std::string> & shapeModeFileNames =
fileNamesCreator->GetFileNames();
for (unsigned int k = 0; k < numberOfPCAModes; ++k)
{
ReaderType::Pointer shapeModeReader = ReaderType::New();
shapeModeReader->SetFileName(shapeModeFileNames[k].c_str());
shapeModeReader->Update();
shapeModeImages[k] = shapeModeReader->GetOutput();
}
shape->SetMeanImage(meanShapeReader->GetOutput());
shape->SetPrincipalComponentImages(shapeModeImages);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Further we assume that the shape modes have been normalized
// by multiplying with the corresponding singular value. Hence,
// we can set the principal component standard deviations to all
// ones.
//
// \index{itk::PCAShapeSignedDistanceFunction!Set\-Principal\-Component\-Standard\-Deviations()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ShapeFunctionType::ParametersType pcaStandardDeviations(numberOfPCAModes);
pcaStandardDeviations.Fill(1.0);
shape->SetPrincipalComponentStandardDeviations(pcaStandardDeviations);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, we instantiate a \doxygen{Euler2DTransform} and connect it to the
// PCASignedDistanceFunction. The transform represent
// the pose of the shape. The parameters of the transform
// forms the set of \emph{pose} parameters.
//
// \index{itk::PCAShapeSignedDistanceFunction!SetTransform()}
// \index{itk::ShapeSignedDistanceFunction!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformType = itk::Euler2DTransform<double>;
TransformType::Pointer transform = TransformType::New();
shape->SetTransform(transform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Before updating the level set at each iteration, the parameters
// of the current best-fit shape is estimated by minimizing the
// \doxygen{ShapePriorMAPCostFunction}. The cost function is composed of
// four terms: contour fit, image fit, shape prior and pose prior.
// The user can specify the weights applied to each term.
//
// \index{itk::ShapePriorMAPCostFunction!SetWeights()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using CostFunctionType =
CostFunctionType::Pointer costFunction = CostFunctionType::New();
CostFunctionType::WeightsType weights;
weights[0] = 1.0; // weight for contour fit term
weights[1] = 20.0; // weight for image fit term
weights[2] = 1.0; // weight for shape prior term
weights[3] = 1.0; // weight for pose prior term
costFunction->SetWeights(weights);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Contour fit measures the likelihood of seeing the current
// evolving contour for a given set of shape/pose parameters.
// This is computed by counting the number of pixels inside
// the current contour but outside the current shape.
//
// Image fit measures the likelihood of seeing certain image
// features for a given set of shape/pose parameters. This is
// computed by assuming that ( 1 - edge potential ) approximates
// a zero-mean, unit variance Gaussian along the normal of
// the evolving contour. Image fit is then computed by computing
// the Laplacian goodness of fit of the Gaussian:
//
// \begin{equation}
// \sum \left( G(\psi(\mathbf{x})) - |1 - g(\mathbf{x})| \right)^2
// \end{equation}
//
// where $G$ is a zero-mean, unit variance Gaussian and $g$ is
// the edge potential feature image.
//
// The pose parameters are assumed to have a uniform distribution
// and hence do not contribute to the cost function.
// The shape parameters are assumed to have a Gaussian distribution.
// The parameters of the distribution are user-specified. Since we
// assumed the principal modes have already been normalized,
// we set the distribution to zero mean and unit variance.
//
// \index{itk::ShapePriorMAPCostFunction!SetShapeParameterMeans()}
// \index{itk::ShapePriorMAPCostFunction!SetShapeParameterStandardDeviations()}
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
CostFunctionType::ArrayType mean(shape->GetNumberOfShapeParameters());
CostFunctionType::ArrayType stddev(shape->GetNumberOfShapeParameters());
mean.Fill(0.0);
stddev.Fill(1.0);
costFunction->SetShapeParameterMeans(mean);
costFunction->SetShapeParameterStandardDeviations(stddev);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example, we will use the
// \doxygen{OnePlusOneEvolutionaryOptimizer} to optimize the cost function.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
OptimizerType::Pointer optimizer = OptimizerType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The evolutionary optimization algorithm is based on testing
// random permutations of the parameters. As such, we need to provide
// the optimizer with a random number generator. In the following lines,
// we create a \doxygen{NormalVariateGenerator}, seed it, and
// connect it to the optimizer.
//
// \index{itk::Statistics::NormalVariateGenerator!Initialize()}
// \index{itk::OnePlusOneEvolutionaryOptimizer!SetNormalVariateGenerator()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
GeneratorType::Pointer generator = GeneratorType::New();
generator->Initialize(20020702);
optimizer->SetNormalVariateGenerator(generator);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The cost function has $K+3$ parameters. The first $K$
// parameters are the principal component multipliers, followed
// by the 2D rotation parameter (in radians) and the x- and
// y- translation parameters (in mm). We need to carefully
// scale the different types of parameters to compensate
// for the differences in the dynamic ranges of the parameters.
//
// \index{itk::OnePlusOneEvolutionaryOptimizer!SetScales()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ScalesType scales(shape->GetNumberOfParameters());
scales.Fill(1.0);
for (unsigned int k = 0; k < numberOfPCAModes; k++)
{
scales[k] = 20.0; // scales for the pca mode multiplier
}
scales[numberOfPCAModes] = 350.0; // scale for 2D rotation
optimizer->SetScales(scales);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, we specify the initial radius, the shrink and
// grow mutation factors and termination criteria of the optimizer.
// Since the best-fit shape is re-estimated each iteration of
// the curve evolution, we do not need to spend too much time finding the
// true minimizing solution each time; we only need to head towards it. As
// such, we only require a small number of optimizer iterations.
//
// \index{itk::OnePlusOneEvolutionaryOptimizer!Initialize()}
// \index{itk::OnePlusOneEvolutionaryOptimizer!SetEpsilon()}
// \index{itk::OnePlusOneEvolutionaryOptimizer!SetMaximumIteration()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
double initRadius = 1.05;
double grow = 1.1;
double shrink = pow(grow, -0.25);
optimizer->Initialize(initRadius, grow, shrink);
optimizer->SetEpsilon(1.0e-6); // minimal search radius
optimizer->SetMaximumIteration(15);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Before starting the segmentation process we need to also supply the
// initial best-fit shape estimate. In this example, we start with the
// unrotated mean shape with the initial x- and y- translation specified
// through command-line arguments.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ShapeFunctionType::ParametersType parameters(
shape->GetNumberOfParameters());
parameters.Fill(0.0);
parameters[numberOfPCAModes + 1] = std::stod(argv[16]); // startX
parameters[numberOfPCAModes + 2] = std::stod(argv[17]); // startY
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, we connect all the components to the filter and add our
// observer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
geodesicActiveContour->SetShapeFunction(shape);
geodesicActiveContour->SetCostFunction(costFunction);
geodesicActiveContour->SetOptimizer(optimizer);
geodesicActiveContour->SetInitialParameters(parameters);
using CommandType = CommandIterationUpdate<GeodesicActiveContourFilterType>;
CommandType::Pointer observer = CommandType::New();
geodesicActiveContour->AddObserver(itk::IterationEvent(), observer);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The invocation of the \code{Update()} method on the writer triggers the
// execution of the pipeline. As usual, the call is placed in a
// \code{try/catch} block to handle exceptions should errors occur.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
writer->Update();
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Print out some useful information
std::cout << std::endl;
std::cout << "Max. no. iterations: "
<< geodesicActiveContour->GetNumberOfIterations() << std::endl;
std::cout << "Max. RMS error: "
<< geodesicActiveContour->GetMaximumRMSError() << std::endl;
std::cout << std::endl;
std::cout << "No. elpased iterations: "
<< geodesicActiveContour->GetElapsedIterations() << std::endl;
std::cout << "RMS change: " << geodesicActiveContour->GetRMSChange()
<< std::endl;
std::cout << "Parameters: " << geodesicActiveContour->GetCurrentParameters()
<< std::endl;
writer4->Update();
// The following writer type is used to save the output of the time-crossing
// map in a file with apropiate pixel representation. The advantage of
// saving this image in native format is that it can be used with a viewer
// to help determine an appropriate threshold to be used on the output of
// the fastmarching filter.
//
using InternalWriterType = itk::ImageFileWriter<InternalImageType>;
InternalWriterType::Pointer mapWriter = InternalWriterType::New();
mapWriter->SetInput(fastMarching->GetOutput());
mapWriter->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput4.mha");
mapWriter->Update();
InternalWriterType::Pointer speedWriter = InternalWriterType::New();
speedWriter->SetInput(reciprocal->GetOutput());
speedWriter->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput3.mha");
speedWriter->Update();
InternalWriterType::Pointer gradientWriter = InternalWriterType::New();
gradientWriter->SetInput(gradientMagnitude->GetOutput());
gradientWriter->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput2.mha");
gradientWriter->Update();
// Also write out the initial and final best fit shape
using EvaluatorFilterType =
InternalImageType,
InternalImageType>;
EvaluatorFilterType::Pointer evaluator = EvaluatorFilterType::New();
evaluator->SetInput(geodesicActiveContour->GetOutput());
evaluator->SetFunction(shape);
shape->SetParameters(geodesicActiveContour->GetInitialParameters());
thresholder->SetInput(evaluator->GetOutput());
writer->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput5.png");
writer->Update();
shape->SetParameters(geodesicActiveContour->GetCurrentParameters());
evaluator->Modified();
writer->SetFileName(
"GeodesicActiveContourShapePriorImageFilterOutput6.png");
writer->Update();
// Software Guide : BeginLatex
//
// Deviating from previous examples, we will demonstrate this example using
// \code{BrainMidSagittalSlice.png}
// (Figure~\ref{fig:GeodesicActiveContourShapePriorImageFilterOutput}, left)
// from the \code{Examples/Data} directory.
// The aim here is to segment the corpus callosum from the image using a
// shape model defined by \code{CorpusCallosumMeanShape.mha} and the first
// three principal components \code{CorpusCallosumMode0.mha},
// \code{CorpusCallosumMode1.mha} and \code{CorpusCallosumMode12.mha}. As
// shown in Figure~\ref{fig:CorpusCallosumPCAModes}, the first mode captures
// scaling, the second mode captures the shifting of mass between the
// rostrum and the splenium and the third mode captures the degree of
// curvature. Segmentation results with and without shape guidance are shown
// in Figure~\ref{fig:GeodesicActiveContourShapePriorImageFilterOutput2}.
//
//
// \begin{figure} \center
// \includegraphics[width=0.30\textwidth]{BrainMidSagittalSlice}
// \includegraphics[width=0.30\textwidth]{GeodesicActiveContourShapePriorImageFilterOutput5}
// \itkcaption[GeodesicActiveContourShapePriorImageFilter input image and
// initial model]{ The input image to the
// GeodesicActiveContourShapePriorLevelSetImageFilter is a synthesized MR-T1
// mid-sagittal slice ($217 \times 180$ pixels, $1 \times 1$ mm spacing) of
// the brain (left) and the initial best-fit shape (right) chosen to roughly
// overlap the corpus callosum in the image to be segmented.}
//
// \label{fig:GeodesicActiveContourShapePriorImageFilterOutput}
// \end{figure}
//
//
// \begin{figure}
// \center
// \begin{tabular}{cccc}
// & $-3\sigma$ & mean & $+3\sigma$ \\ mode 0: &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumModeMinus0} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumMeanShape} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumModePlus0} \\ mode
// 1: & \includegraphics[width=0.10\textwidth]{CorpusCallosumModeMinus1} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumMeanShape} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumModePlus1} \\ mode
// 2: & \includegraphics[width=0.10\textwidth]{CorpusCallosumModeMinus2} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumMeanShape} &
// \includegraphics[width=0.10\textwidth]{CorpusCallosumModePlus2} \\
// \end{tabular} \itkcaption[Corpus callosum PCA modes]{First three PCA
// modes of a low-resolution
// ($58 \times 31$ pixels, $2 \times 2$ mm spacing) corpus callosum model
// used in the shape guided geodesic active contours example.}
//
// \label{fig:CorpusCallosumPCAModes}
// \end{figure}
//
//
//
// A sigma value of $1.0$ was used to compute the image gradient and the
// propagation and shape prior scaling are respectively set to $0.5$ and
// $0.02$. An initial level set was created by placing one seed point in the
// rostrum $(60,102)$, one in the splenium $(120, 85)$ and one
// centrally in the body $(88,83)$ of the corpus callosum with
// an initial radius of $6$ pixels at each seed position.
// The best-fit shape was initially placed with a translation of
// $(10,0)$mm so that it roughly overlapped
// the corpus callosum in the image as shown in
// Figure~\ref{fig:GeodesicActiveContourShapePriorImageFilterOutput}
// (right).
//
//
// From Figure~\ref{fig:GeodesicActiveContourShapePriorImageFilterOutput2}
// it can be observed that without shape guidance (left), segmentation using
// geodesic active contour leaks in the regions where the corpus callosum
// blends into the surrounding brain tissues. With shape guidance (center),
// the segmentation is constrained by the global shape model to prevent
// leaking.
//
// The final best-fit shape parameters after the segmentation process is:
//
// \begin{verbatim}
// Parameters: [-0.384988, -0.578738, 0.557793, 0.275202, 16.9992, 4.73473]
// \end{verbatim}
//
// and is shown in
// Figure~\ref{fig:GeodesicActiveContourShapePriorImageFilterOutput2}
// (right). Note that a $0.28$ radian ($15.8$ degree) rotation has been
// introduced to match the model to the corpus callosum in the image.
// Additionally, a negative weight for the first mode shrinks the size
// relative to the mean shape. A negative weight for the second mode shifts
// the mass to splenium, and a positive weight for the third mode increases
// the curvature. It can also be observed that the final segmentation is a
// combination of the best-fit shape with additional local deformation. The
// combination of both global and local shape allows the segmentation to
// capture fine details not represented in the shape model.
//
//
// \begin{figure} \center
// \includegraphics[width=0.30\textwidth]{GeodesicActiveContourShapePriorImageFilterOutput1}
// \includegraphics[width=0.30\textwidth]{GeodesicActiveContourShapePriorImageFilterOutput2}
// \includegraphics[width=0.30\textwidth]{GeodesicActiveContourShapePriorImageFilterOutput6}
// \itkcaption[GeodesicActiveContourShapePriorImageFilter
// segmentations]{Corpus callosum segmentation using geodesic active
// contours without (left) and with (center) shape guidance. The image on
// the right represents the best-fit shape at the end of the segmentation
// process.}
//
// \label{fig:GeodesicActiveContourShapePriorImageFilterOutput2}
// \end{figure}
//
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkSpatialFunctionImageEvaluatorFilter.h
itkEuler2DTransform.h
itk::PCAShapeSignedDistanceFunction
Compute the signed distance from a N-dimensional PCA Shape.
Definition: itkPCAShapeSignedDistanceFunction.h:67
itk::BinaryThresholdImageFilter
Binarize an input image by thresholding.
Definition: itkBinaryThresholdImageFilter.h:139
itk::ShapePriorMAPCostFunction
Represents the maximum aprior (MAP) cost function used ShapePriorSegmentationLevelSetImageFilter to e...
Definition: itkShapePriorMAPCostFunction.h:50
itkImageFileReader.h
itk::SmartPointer< Self >
itk::ChangeInformationImageFilter
Change the origin, spacing and/or region of an Image.
Definition: itkChangeInformationImageFilter.h:49
itk::BoundedReciprocalImageFilter
Computes 1/(1+x) for each pixel in the image.
Definition: itkBoundedReciprocalImageFilter.h:73
itkFastMarchingImageFilter.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:44
itk::CurvatureAnisotropicDiffusionImageFilter
This filter performs anisotropic diffusion on a scalar itk::Image using the modified curvature diffus...
Definition: itkCurvatureAnisotropicDiffusionImageFilter.h:58
itk::Statistics::NormalVariateGenerator
Normal random variate generator.
Definition: itkNormalVariateGenerator.h:98
itkNumericSeriesFileNames.h
itkCurvatureAnisotropicDiffusionImageFilter.h
itk::SpatialFunctionImageEvaluatorFilter
Evaluates a SpatialFunction onto a source image.
Definition: itkSpatialFunctionImageEvaluatorFilter.h:43
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkBoundedReciprocalImageFilter.h
itk::FastMarchingImageFilter
Solve an Eikonal equation using Fast Marching.
Definition: itkFastMarchingImageFilter.h:136
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:41
itk::GradientMagnitudeRecursiveGaussianImageFilter
Computes the Magnitude of the Gradient of an image by convolution with the first derivative of a Gaus...
Definition: itkGradientMagnitudeRecursiveGaussianImageFilter.h:50
itkOnePlusOneEvolutionaryOptimizer.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::NumericSeriesFileNames::New
static Pointer New()
itk::Euler2DTransform
Euler2DTransform of a vector space (e.g. space coordinates)
Definition: itkEuler2DTransform.h:41
itkImageFileWriter.h
itk::GeodesicActiveContourShapePriorLevelSetImageFilter
Segments structures in an image based on a user supplied edge potential map and user supplied shape m...
Definition: itkGeodesicActiveContourShapePriorLevelSetImageFilter.h:111
itkNormalVariateGenerator.h
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:60
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Math::e
static constexpr double e
Definition: itkMath.h:54
itkBinaryThresholdImageFilter.h
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
itk::OnePlusOneEvolutionaryOptimizer
1+1 evolutionary strategy optimizer
Definition: itkOnePlusOneEvolutionaryOptimizer.h:71
itkGeodesicActiveContourShapePriorLevelSetImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itkChangeInformationImageFilter.h
itkPCAShapeSignedDistanceFunction.h
itkGradientMagnitudeRecursiveGaussianImageFilter.h